#casp14 search results
The leap forward in protein structure prediction by @DeepMind's AlphaFold2 in #CASP14 highlights a potential to revolutionise structure determination. The open availability of 170,000+ structures in the PDB has been harnessed to solve a problem that stretches back decades
In a major scientific breakthrough, the latest version of #AlphaFold has been recognised as a solution to one of biology's grand challenges - the “protein folding problem”. It was validated today at #CASP14, the biennial Critical Assessment of protein Structure Prediction (1/3)
AlphaFold 2 予測構造 30 連発。 緑: 実験により測定された正解構造 水色: AlphaFold 2 による予測構造 昨夜結果が発表され、話題をさらっているタンパク質立体構造予測コンテスト #CASP14 。ダントツ１位をもぎとった DeepMind チームの予測プログラム AlphaFold 2 の革新的性能を眺めてみよう。
Biology's ImageNet moment has arrived: #AlphaFold2 solves Protein Folding at the #CASP14 competition! Blog: deepmind.com/blog/article/a…
Last time, #alphafold made headlines by beating 2nd place by 18.5%. In #CASP14, #alphafold2 essentially beat SOTA by 165.2%. Results are astounding: demonstrate the power of large sequence databases (+retrieval) and attention-based learning.
Seems like AlphaFold2 is the undisputed winner at this year's #CASP14 on regular targets. Can't wait for this algorithm to be available!
Protein folding competition #CASP15 has followed the same pattern observed in computer vision back in 2012, revolutionized by Deep Learning. Following dominance of #AlphaFold2 in #CASP14, all top methods this year build on top of AF2.
Completing the sprint to deliver on the commitments we made at #CASP14 last Dec, we’ve published 2 papers in @Nature, open-sourced #AlphaFold's code and provided a colab. 4/ dpmd.ai/nature-proteome dpmd.ai/nature-methods dpmd.ai/alphafold-os dpmd.ai/alphafold-colab
Tento čtvrtek budeme s Davidem Hokszou z @matfyz a @vaclav_veverka z @IOCBPrague diskutovat v live stream (facebook.com/events/1475531…) o průlomu v predikci proteinové struktury a jeho důsledcích. Přidejdte se ! #AlphaFold #casp14 @UniKarlova
‘It will change everything’: @DeepMind's #AI makes gigantic leap in solving protein structures h/t @demishassabis cc: @DeepLearn007 #CASP14 nature.com/articles/d4158…
Congratulations to this year’s #CASP15 winners and participants for their achievements! 🧬 In the two years since #CASP14, we’ve been excited to see the advances being made in predicting protein structures, building on #AlphaFold and bringing in new ideas.
even #AlphaFold2 worst model does not differ much from experimentally solved structure. Breathtaking! #CASP14
Hey @DeepMind - amazing work in #CASP14 protein folding! If you're ready for the next challenge we just started the 7th #CSPBlindTest - small molecule crystal structure prediction. Interested? hubs.ly/H0BWqx70 #crystallography #computationalchemistry #chemistry
Esto puede suponer una revolución: el plegamiento de #proteínas de #AlphaFold 2 vence de forma rotunda en #CASP14 • francis.naukas.com/2020/12/03/el-… por @emulenews
Presentation of the results of the evaluation of #casp14 3D contact prediction category by @Albalepore @BSC_CNS Amazing sustained 70% accuracy on the very challenging targets. and equally amazing work done by Alba and her BSC_CASPassesment team.
The success of #AlphaFold2 at #CASP14 is a testament to how far AI has come in aiding scientific discovery. A giant leap for biology!
This is huge! AlphaFold2 solving the protein folding problem is a major breakthrough in biology. Exciting times ahead! 🧬🧪 #AlphaFold2 #CASP14 #ProteinFolding #Biology #DeepMind
🗣@Alfons_Valencia "After what we saw as assessors of #CASP14 it was easy to predict that this will happen sooner than later. Congratulations - your work is driving and transforming our community!"
Premio Fronteras del Conocimiento en #Biomedicina a David Baker, Demis Hassabis y John Jumper por revolucionar el estudio y diseño de proteínas con la Inteligencia Artificial e impulsar mediante esta tecnología el desarrollo de nuevos tratamientos bbva.info/3j5JeSK
#AlphaFold es el sistema de Inteligencia Artificial que en 2018 y 2020 logró determinar con gran precisión el plegamiento de proteínas a partir de una secuencia de aminoácidos dada, en el CASP (Critical Assessment of Protein Structure Prediction). #CASP13 #CASP14
Protein folding competition #CASP15 has followed the same pattern observed in computer vision back in 2012, revolutionized by Deep Learning. Following dominance of #AlphaFold2 in #CASP14, all top methods this year build on top of AF2.
Slides of the different #CASP15 presentations available for download. Slide below shows how #CASP14 (black line) was truly the renaissance moment of protein folding
So, the significant assembly pred. improvement we witness in #CASP15 is a reflection of the tertiary structure pred. revolution observed in #CASP14. Even so, smart tweaks performed by several academic and industry groups was needed to push the multimeric modeling limits further.
Over these targets, community produced extremely good results, when we consider the interface patch (IPS) and interface contact (ICS) scores. In #CASP15, for almost 50% of the cases, there is at least one high accuracy model generated (ICS>=0.8), which was only 7% in #CASP14.
Compared to #CASP14, in #CASP15, we have a significant increase in the number of targets offered and in the number of groups participating the challange:
Congratulations to this year’s #CASP15 winners and participants for their achievements! 🧬 In the two years since #CASP14, we’ve been excited to see the advances being made in predicting protein structures, building on #AlphaFold and bringing in new ideas.
#AlphaFold 2.0, released in 2020, has been called the gold standard for predicting protein structures and is the first computational method to achieve near experimental accuracy with a Global Distance Score averaging 92.4 in #CASP14- predicting structures to the nearest atom 2/6
Credit is also due to the many, many computational structural biologists that have worked over the past several decades to develop and improve algorithms to predict protein 3D structure given a linear amino acid sequence, facilitated by competitions such as #CASP14.
In #CASP14, 39 research groups submitted more than 2500 3D models on 22 protein complexes. The majority of these targets came from bacterial and viral systems with >=3 monomers. To give you an idea of the round, here are the top five successfully predicted targets. (2/8)
Before going into the details of what #AlphaFold2 could bring into the assembly prediction field, here is our take (with @burcuozdenb and Andriy Kryshtafovych) what the field already looks like, according to the #CASP14 round: onlinelibrary.wiley.com/doi/epdf/10.10… (1/8)
The day has come already before the CASP14 Proteins issue is officially out! Now, it is time to read carefully, what AlphaFold is offering to the multimer prediction field 👇
I’m happy to share the results of our contact and distance assessment in #CASP14 recently published onlinelibrary.wiley.com/doi/epdf/10.10… @Alfons_Valencia @Albalepore @cftpontes @EMilanetti #AndriyKryshtafovych
On it, we analyse the results of the #CASP14 competition and its impact on molecular replacement. A novelty on it is that we also propose a new metric, conceived by @RandyJRead, the relative expected log-likelihood gain, that does not require experimental data. (2/5)
AI Can Compute Protein Structures in 10 Minutes bit.ly/3Be87zV #protein #DeepMind #CASP14
#alphafold has been validated in the blind test competition to predict known structures before they are public #CASP14 - of course not perfect and with some limitations, but helpfully alphafold also predicts where it is likely to be less accurate.
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